Systems and methods for constrained optimization of a hybrid power system that accounts for asset maintenance and degradation

ABSTRACT

Systems and methods for operating a hybrid power system are disclosed. A controller may perform operations, including: obtaining load data for the hybrid power system; obtaining power availability data and energy cost data for each power asset in each power asset group of a plurality of power asset groups; and determining active power commands for each power asset by performing at least one optimization, such that the determined active power commands optimize a total operating cost, wherein: the at least one optimization is based on at least one cost function that accounts for asset degradation, asset maintenance cost, asset operation efficiency cost, and the energy cost data; and the at least one optimization is constrained by a plurality of constraints based on the load data, the power availability data, and characteristics of the power assets; and operating each power asset based on the determined active power commands.

TECHNICAL FIELD

Various embodiments of this disclosure relate generally to hybrid powersystems control, and, more particularly, to systems and methods foroptimizing hybrid power systems control.

BACKGROUND

Hybrid power systems, e.g., power supply systems that incorporatemultiple modes of electricity generation and/or storage, may have manybenefits, such as reduced power supply costs or emissions, and/orimproved sustainability, reliability, redundancy, or the like. However,managing multiple types of power assets may be complex, and thus it maybe difficult to operate a hybrid power system at its full potential.Further, the complexity for managing a hybrid power system may scalerapidly as the number of power assets and power asset types increase.

Different approaches to this control problem have been developed. Someapproaches utilize a rule-based algorithm to make power distributiondecisions over a plurality of power assets. However, such approachesoften miss edge cases, and are generally sub-optimal due to thedifficulty of describing such a complex problem space with rules. Someapproaches apply optimization techniques. However, the complexity ofhybrid power systems may result in optimization being computationallyexpensive. Additionally, conventional optimization techniques may notaccount for aspects of power assets that are type-specific, such asmaintenance or replacement, asset degradation, or the like.

U.S. Patent Publication No. 2020/0198495 A1 describes a real-time energymanagement strategy for hybrid electric vehicles with reduced batteryaging. This reference discloses adjusting the use of energy sources in ahybrid power system by an Adaptive Equivalent Consumption ManagementStrategy (A-ECMS) implemented on a supervisory controller. The A-ECMSmay take into account both fuel economy and battery capacity degradationto optimize fuel consumption with consideration of battery aging.However, this approach may not account for various aspects of powerassets that are type-specific, and moreover does not address the issueof computational complexity as the number of power assets increases.

The disclosed method and system may solve one or more of the problemsset forth above and/or other problems in the art. The scope of thecurrent disclosure, however, is defined by the attached claims, and notby the ability to solve any specific problem.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, methods and systems aredisclosed for optimization of hybrid power control system.

In one aspect, a method of operating a hybrid power system may include:obtaining load data for the hybrid power system; obtaining poweravailability data and energy cost data for each power asset in eachpower asset group of a plurality of power asset groups; and determiningactive power commands for each power asset by performing at least oneoptimization, such that the determined active power commands optimize atotal operating cost of the hybrid power system, wherein: the at leastone optimization is based on at least one cost function that accountsfor asset degradation, asset maintenance cost, asset operationefficiency cost, and the energy cost data; and the at least oneoptimization is constrained by a plurality of constraints based on theload data, the power availability data, and characteristics of the powerassets; and operating each power asset based on the determined activepower commands.

In another aspect, a controller for a hybrid power system may include:at least one memory storing instructions; and at least one processoroperatively connected to the memory, and configured to execute theinstructions to perform operations. The operations may include:obtaining load data for the hybrid power system; obtaining poweravailability data and energy cost data for each power asset in eachpower asset group of a plurality of power asset groups; and determiningactive power commands for each power asset by performing at least oneoptimization, such that the determined active power commands optimize atotal operating cost of the hybrid power system, wherein: the at leastone optimization is based on at least one cost function that accountsfor asset degradation, asset maintenance cost, asset operationefficiency cost, and the energy cost data; and the at least oneoptimization is constrained by a plurality of constraints based on theload data, the power availability data, and characteristics of the powerassets; and operating each power asset based on the determined activepower commands.

In a further aspect, A hybrid power system may include: a plurality ofpower asset groups and a controller. The plurality of power asset groupsmay include two or more of a genset group, an energy storage systemgroup, a photovoltaic group, or a power grid connection. The controllermay include: at least one memory storing instructions; and at least oneprocessor operatively connected to the memory, and configured to executethe instructions to perform operations. The operations may include:obtaining load data for the hybrid power system; obtaining poweravailability data and energy cost data for each power asset in eachpower asset group of a plurality of power asset groups; and determiningactive power commands for each power asset by performing at least oneoptimization, such that the determined active power commands optimize atotal operating cost of the hybrid power system, wherein: the at leastone optimization is based on at least one cost function that accountsfor asset degradation, asset maintenance cost, asset operationefficiency cost, and the energy cost data; and the at least oneoptimization is constrained by a plurality of constraints based on theload data, the power availability data, and characteristics of the powerassets; and operating each power asset based on the determined activepower commands.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts a schematic of an exemplary hybrid power system,according to one or more embodiments.

FIG. 2 depicts a schematic of an exemplary controller of the hybridpower system of FIG. 1 , according to one or more embodiments.

FIG. 3 depicts a flowchart of an exemplary method of operating a hybridpower system, according to one or more embodiments.

FIG. 4 depicts a flowchart of another exemplary method of operating ahybrid power system, according to one or more embodiments.

FIG. 5 depicts an example of a computing device, according to one ormore embodiments.

DETAILED DESCRIPTION

Both the foregoing general description and the following detaileddescription are exemplary and explanatory only and are not restrictiveof the features, as claimed. As used herein, the terms “comprises,”“comprising,” “having,” including,” or other variations thereof, areintended to cover a non-exclusive inclusion such that a process, method,article, or apparatus that comprises a list of elements does not includeonly those elements, but may include other elements not expressly listedor inherent to such a process, method, article, or apparatus. The term“or” is used disjunctively, such that “at least one of A or B” includes,A, B, A and A, A and B, etc. Moreover, in this disclosure, relativeterms, such as, for example, “about,” “substantially,” “generally,” and“approximately” are used to indicate a possible variation of ±10% in thestated value.

As used herein, an “on-line” activity generally encompasses an activitythat is performed live, during operation, on an instantaneous basis,continuously, in real or near real-time, or the like. A “prospective”activity generally encompasses an activity for which the result orimplementation of which is scheduled, is not in or is not updated inreal or near real-time, is at least partially non-instantaneous,non-continuous, pertains to forecasting, predictions, a result that isprognostic or that accounts for a range of time, or the like. The term“power asset” generally encompasses a system or device for generating,storing, and/or supplying electrical power. Examples of a power assetinclude, but are not limited to, gensets (e.g., a combination of anengine and electrical generator used to produce electrical power), aphotovoltaic (e.g., a “PV” or solar) cell, an energy storage system suchas a battery, a fuel cell, a power grid connection, a wind turbine, ahydro-electric generator, a turbine generator, a reactor, etc.

Reference to any particular activity is provided in this disclosure onlyfor convenience and not intended to limit the disclosure. A person ofordinary skill in the art would recognize that the concepts underlyingthe disclosed devices and methods may be utilized in any suitableactivity. The disclosure may be understood with reference to thefollowing description and the appended drawings, wherein like elementsare referred to with the same reference numerals.

In one aspect, a hybrid power system of a micro-grid may include acontroller configured to optimize operation of various power assets ofdifferent types, e.g., minimize cost of operation, reduce emissions,maintain integrity of the power assets, or the like. To do so, thecontroller may perform a plurality of different optimizations. Forexample, the controller may perform at least one prospectiveoptimization that is prognostic, e.g., that considers forecasts for theload of the hybrid power system, the energy cost or power availabilityof various power assets, or the like in order to determine an optimalschedule for operation of the power assets over a future period of time.The prospective optimization may be performed periodically, e.g., onceevery fifteen or thirty minutes, once per hour, once per day, etc. Theprospective optimization may pertain to a moving future period of time,e.g., an hour, a day, etc., looking ahead from a time at which theprospective optimization is performed. The resulting schedule mayinclude active power commands that describe how a particular power assetis to be operated, e.g., when to turn on or off, when to charge ordischarge, etc. In another example, the controller may perform at leastone on-line optimization that, e.g., instead of relying on forecasteddata, uses on-line data to make real-time or near real-time adjustmentsto the schedule. For instance, power available from a PV asset may beless than expected due to cloud cover, the hybrid power system mayexperience a greater than anticipated load, or a cost of grid power orof fuel for a genset may deviate from the forecasted data. The on-lineoptimization may be used to find an optimal usage of the various powerassets of the hybrid power system to account for any such discrepancies.In one aspect, dividing the optimization problem into prospective andon-line optimizations may reduce the computational complexity of theoptimization. In another aspect, dividing the optimization problem mayenable results that account for future events, e.g., over a period oftime, while also accounting for more immediate situations that may nototherwise be captured in a prospective-only approach, such asforecasting errors, course correction, or the like.

In another aspect, the controller of the hybrid power system may beconfigured to perform one or more optimizations that account for variouscharacteristics of power assets that are type-specific, such as assetdegradation, asset maintenance, and particular aspects of differentasset types that may impact operating efficiency.

In a further aspect, the controller of the hybrid power system may beconfigured to perform one or more constrained optimizations. Constraintsfor one or more optimizations performed by the controller may accountfor or be associated with various aspects of the hybrid power systemsuch as, for example, the load experienced by the hybrid power system,capacities or ratings of the various power assets, characteristics oroperational limitations of the various power assets, resiliency orredundancy parameters, or the like. In some instances, at least aportion of the constraints for the one or more optimizations may be softconstraints, e.g., constraints that weigh in to the optimization butthat are not absolute requirements. In some instances, the constraintsfor the one or more optimizations may be segmented into groups ofdifferent priorities.

While several of the examples above involve of a micro-grid, it shouldbe understood that techniques according to this disclosure may beadapted to any suitable type of application for a hybrid power systemsuch as, for example, a vehicle power plant (e.g., car, ship, train,etc.), a power source for a building or facility, or the like. It shouldalso be understood that the examples above are illustrative only. Thetechniques and technologies of this disclosure may be adapted to anysuitable activity.

FIG. 1 depicts an exemplary hybrid power system 100 that may be utilizedwith techniques presented herein. One or more user device(s) 105, a load110, a plurality of power asset groups 115, one or more sensor(s) 120,and one or more data resource device(s) 125 may be operatively connectedto each other and/or may communicate across an electronic network 130.As will be discussed in further detail below, one or more controller(s)135 may communicate with one or more of the other components of thehybrid power system 100 across electronic network 130. The one or moreuser device(s) 105 may be associated with a user 140, e.g., a userassociated with one or more of managing, maintaining, inspecting,repairing, operating, or controlling the hybrid power system 100, or thelike.

The user device 105 may be configured to enable the user 140 to accessand/or interact with other devices in the hybrid power system 100. Forexample, the user device 105 may be a computer system such as, forexample, a desktop computer, a mobile device, a tablet, etc. In someembodiments, the user device 105 may include a client hosted on one ormore remote systems, e.g., in a cloud architecture, distributedcomputing cluster, or the like. In some embodiments, the user device 105may include and/or access an embedded controller, an applicationspecific circuit or processor, or the like. In some embodiments, theuser device 105 may include one or more electronic application(s), e.g.,a program, plugin, browser extension, etc., installed on a memory of theuser device 105. In some embodiments, the electronic application(s) maybe associated with one or more of the other components in the hybridpower system 100. For example, the electronic application(s) may includeone or more of system control software, system monitoring software,scheduling tools, load analysis tools, forecasting tools, etc. Theelectronic application(s), such as the foregoing examples, may beconfigured to enable a user to select, modify, and/or control variousoptions and/or behaviors of the hybrid power system 100. In someembodiments, the user device 105 may be configured to generate,implement, and/or display a Human-Machine-Interface (HMI) for the hybridpower system 100, and/or other information or interactive tools such as,for example, diagnostic processes, forecasting processes, schedulingprocesses, or the like.

The load 110 may include any number of systems, devices, or the like tobe powered by the hybrid control system such as, for example, buildingelectronic power systems, air conditioning systems, machines, (in thecase of vehicles) propulsion devices, or the like. In some instances, aportion of the load 110 may be non-discretionary. In some instances, aportion of the load 110 may be automatic, e.g., a system or device thathas a predetermined schedule of operation. In some instances, a portionof the load 110 may be at least partially predictable, e.g., systems ordevices like air conditioning system that operate in correlation toambient temperature or building electronic power systems that operate incorrelation to business hours, or the like. In some instances, a portionof the load 110 may be user controlled, such as appliances, machines, orthe like. In some instances, a portion of the load 110 may becontrollable by the hybrid power system 100. For example, as discussedin further detail below, in some instances, the controller 135 maydeactivate a portion of the load 110 when the power required by the load110 exceeds power available from the hybrid power system 100.

The plurality of power asset groups 115 may include any suitable numberof power asset groups. In the embodiment of the hybrid power system 100depicted in FIG. 1 , the plurality of power asset groups 115 includes agenset group 145, a PV group 150, an energy storage system group 155,and a power grid connection 160. It should be understood that in variousembodiments, various power asset groups may be included or omitted in ahybrid power system instead of or in addition to the groups listedabove. For example, a hybrid power system of a vehicle may not include apower grid connection. However, in some embodiments, a vehicle mayinclude a power grid connection, e.g., in the form of a tether, trolleypantograph, electrified rail, etc. It should also be understood that thepower asset groups listed above are exemplary only, and any suitablepower asset group or groups may be included in any suitable arrangement.Illustrative examples of further power asset groups include a windturbine group, a fuel cell group, a reactor group, etc. A hybrid powersystem according to one or more embodiments may include any suitablenumber of power asset groups, e.g., 2, 5, 10, 50, 250, etc., differentgroups. In some embodiments, different power asset groups may be of asame type, e.g., a plurality of different genset groups. In someembodiments, power asset groups may be hierarchical, e.g., a first powerasset group may include as members a plurality of power assetsub-groups.

A power asset group may include any suitable number of power assets,e.g, 1 (such as, for example, some instances of a power grid connectionor reactor), 5, 10, 100, etc. Power assets within a power asset groupmay be operatively connected within the hybrid power system 100 in anysuitable manner. For example, in some instances, power assets within apower asset group may be connected in one or more banks, e.g., inparallel or in series. In some embodiments, individual power assets maybe individually connected, or may be connected to the hybrid powersystem 100 via intermediary devices such as a transformer, asub-station, an inverter, a rectifier, a load balancer, an electricalbus, a tie breaker, or the like.

In some embodiments, a power asset may include and/or be integrated withone or more sensor 120. For example, a power asset may include a sensorconfigured to detect one or more of or power output, voltage, frequency,ambient temperature, operating temperature, operational duration, etc.

The genset group 145 may include a plurality of gensets 146. The gensets146 may have operational characteristics such as apparent power limits,active power rating limits, power factor range limits, a predetermined,regulated, and/or designed minimum load capacity, a start/stop frequencylimit or threshold, a maximum load capacity, total operational lifetime,current operational age, fuel consumption rate, power output,maintenance cost, replacement cost, etc. Such characteristics may bepredetermined, e.g., set during manufacture or established viaregulatory requirement, or may vary over the course of operation or thelifetime of the genset(s). As discussed in further detail below, one ormore aspects of such characteristics (e.g., one or more fuel consumptionmap(s)) may be sensed (e.g., via sensor(s) 120), simulated, mapped,tracked, and/or predicted (e.g., via the data resource device 125, thecontroller 135, or the like). As discussed in further detail below, suchoperations may occur in a background setting, e.g., at a slower ratethan prospective operations discussed elsewhere in this disclosure.

The PV group 150 may include a plurality of PV devices 151, e.g., cells,banks or cells, or the like. The PV devices 151 may be characterized bymaximum power output, a relation between irradiance of the PV device 151and power output, a device lifetime, a device age, a replacement cost,etc. As discussed in further detail below, one or more aspects of suchcharacteristics may be sensed (e.g., via sensor(s) 120), simulated,mapped, tracked, and/or predicted (e.g., via the data resource device125, the controller 135, or the like). As discussed in further detailbelow, one or more aspects of such characteristics (e.g., cloudcoverage, weather, temperature, or the like as well as associatedcharacteristics such as irradiance and power capability forecasting) maybe sensed (e.g., via sensor(s) 120), simulated, mapped, tracked, and/orpredicted (e.g., via the data resource device 125, the controller 135,or the like).

The energy storage system group 155 may include a plurality of energystorage systems 156. In the embodiment depicted in the hybrid powersystem 100 in FIG. 1 , the energy storage systems 156 are batteries orbanks of batteries. However, in various embodiments, any suitable typeof energy storage system 156 may be used such as, for example, aflywheel, a thermal energy storage system, pumped hydro-electricstorage, pneumatic energy storage, etc.

The energy storage system 156 may be characterized by a state-of-charge(SOC), depth of discharge (DOD), a discharge energy cost, a chargeenergy cost, total lifetime, replacement cost, calendar aging, cyclingaging, operating temperature, etc. As discussed in further detail below,one or more aspects of such characteristics (e.g., temperature, state ofhealth, age, voltage, current, or the like) may be sensed (e.g., viasensor(s) 120), simulated, mapped, tracked, and/or predicted (e.g., viaa management system of the energy storage system 156 (e.g., a batterymanagement system), the data resource device 125, the controller 135, orthe like).

The power grid connection 160 may be usable to supply power to thehybrid power system 100 from a power grid and/or export power out fromthe hybrid power system 100 into the power grid. The power gridconnection 160 may be characterized by an energy cost for supplyingpower to the hybrid power system 100, an energy revenue for supplyingpower from the hybrid power system 100 to the power grid. In someinstances, the energy cost and energy revenue for the power gridconnection 160 may vary over time, e.g., due to demand, incentives, orother factors. As discussed in further detail below, one or more aspectsof such characteristics (e.g., current and/or day-ahead prices by hourof day or the like, energy import/export limits or rules, energyconcessions, trading, or commitments, etc.) may be retrieved, simulated,mapped, tracked, and/or predicted (e.g., via the data resource device125, the controller 135, or the like).

The sensor(s) 120 may include any suitable number of sensors. The sensor120 may be configured to sense one or more characteristics of one ormore power assets in the plurality of power asset groups 115. Forexample, a temperature sensor may be used to sense a temperature of anenergy storage system 156, a flow meter may be used to sense a fuelconsumption rate of a genset 146, and/or an electrical sensor (e.g., avoltage, current, or power sensor, or the like), may be used to senseone or more aspects of power provided by a particular power asset, powerdrawn by the load 110, or the SOC or DOD of an energy storage system156. A timer may be used to track how long a power asset, e.g., a genset146, has been operating. A fuel meter may sense fuel consumption of agenset and/or genset group. A gas sensor may be used to sense emissions,e.g., from the genset group 145. In some embodiments, power assets ofthe power asset groups 115 may incorporate sensors and/or may beconfigured to output operational data indicative of characteristics ofthe power asset(s).

The data resource device 125 may include a server system, an electronicdata system, computer-readable memory such as a hard drive, flash drive,disk, etc. In some embodiments, the data resource device 125 includesand/or interacts with an application programming interface forexchanging data to other systems, e.g., one or more of the othercomponents of the hybrid power system 100. The data resource device 125may include and/or act as a repository or source for data associatedwith the characteristics of the power assets in the plurality of powerasset groups 115. In various embodiments, the data resource device 125may include one or more of a device manager, device controller, atelematics system (e.g., for off-board data collection), an on-boardand/or off-board data repository, or the like.

The data resource device 125 may be configured to obtain, generate,and/or store data such as, for example, one or more characteristics ofthe power assets in the plurality of power assets 115, characteristicsof the load 110, weather and/or cloud data associated with forecasting apower availability for the PV group 150, costs of fuel for the gensetgroup 145, import and export rates for the power grid connection 160. Insome instances, the data resource device 125 may use historical data togenerate forecast data. For example, the data resource device 125 mayuse historical information about the load 110 in order to generate aload forecast that predicts or estimates an amount of power needed bythe load at, for example, different times of day, different days of theweek, in different seasons, during different weather or ambienttemperature conditions, etc. In another example, historical data may beused to estimate or predict a next day's prices of import and export ofpower via the power grid connection 160, or of costs for fuel for thegenset group 145. In some embodiments, the data resource device 125 mayuse machine learning, e.g., deep learning, to generate forecasts.

The data resource device 125 may be configured to generate and/or obtainan optimal performance map for one or more power assets of the pluralityof power asset groups 115. In various embodiments, an optimalperformance map may be generated based on actual data associated withthe power asset(s) and/or simulation data based on simulation of thepower asset(s). In one example, optimal performance maps may be obtainedthat describe various scenarios of operating different and/or differentnumbers of gensets 146 in the genset group 145. An optimal performancemap may map efficiency and/or cost vs. aggregate power, and/or mayindicate optimal loading of various power assets for different aggregatepower amounts. The optimal performance maps may indicate how much powermay be available from each power asset, the energy cost for each powerasset, or the like, e.g., individually and/or in combination with otherpower assets. In some embodiments, the data resource device 125 may beconfigured to generate, obtain, and/or update the optimal performancemap(s) from time to time, e.g., periodically, and/or in response to atrigger condition such as an indication, e.g., from a sensor 120, thatperformance of a power asset has changed beyond a predeterminedthreshold.

In some embodiments, the optimal performance map(s) and/orcharacteristics of the power asset(s) indicated by the optimalperformance map(s) may be used by the controller 135 when performingoptimizations. While the computational cost of generating or updating anoptimal performance map may be high, such generating or updating mayoccur infrequently relative to the optimization(s) performed by thecontroller 135. The optimal performance map(s) and/or characteristics ofthe power asset(s) indicated by the optimal performance map(s) mayreduce a computational complexity of the optimization(s) performed bythe controller 135.

In various embodiments, the electronic network 130 may be a wide areanetwork (“WAN”), a local area network (“LAN”), personal area network(“PAN”), Ethernet, or the like. In some embodiments, electronic network130 includes the Internet, and information and data provided betweenvarious systems occurs online. “Online” may mean connecting to oraccessing source data or information from a location remote from otherdevices or networks coupled to the Internet. Alternatively, “online” mayrefer to connecting or accessing an electronic network (wired orwireless) via a mobile communications network or device (e.g., fortelematics and/or data collection or transmission. The Internet is aworldwide system of computer networks—a network of networks in which aparty at one computer or other device connected to the network canobtain information from any other computer and communicate with partiesof other computers or devices. The most widely used part of the Internetis the World Wide Web (often-abbreviated “WWW” or called “the Web”).

The controller 135 may include one or more components to monitor, track,and/or control the operation the hybrid power system 100, e.g., thepower assets of the plurality of power asset groups 115. For example,the controller 135 may include a memory 165 and a processor 170.

The memory 165 of the controller 135 may store data and/or software,e.g., instructions, models, algorithms, equations, data tables, or thelike, that are usable and/or executable by the processor 170 to performone or more operations for controlling the hybrid power system 100. Forexample, the controller 135 may be configured to receive input, e.g.,from the plurality of power asset groups 115, the sensor(s) 120, thedata resource device 125 and/or any other suitable source, and generateactive power commands for each of the power assets in the power assetgroups 115 based on the input. For example, the memory 165 may includeone or more optimizer(s) 175 that, when executed by the processor 170,are configured to generate active power commands that optimize theoperation of the hybrid power system 100. Although depicted as a singlecontroller 135 in FIG. 1 , it should be understood that, in variousembodiments, the controller 135 may be distributed across multipledevice and/or may include multiple control modules that operate inconcert.

In some embodiments, the optimizer(s) 175 may be configured to performconstrained optimization. Constraints for one or more optimizationsperformed by the controller 135 may account for or be associated withvarious aspects of the hybrid power system 100 such as, for example, theload 110, capacities or ratings of the various power assets,characteristics or operational limitations of the various power assets,resiliency or redundancy parameters, or the like. In some instances, atleast a portion of the constraints for the one or more optimizations maybe soft constraints, e.g., constraints that weigh in to the optimizationbut that are not absolute requirements. In some instances, theconstraints for the one or more optimizations may be segmented intogroups of different priorities. In the case where not all of theconstraints may be satisfied simultaneously, the controller 135 may beconfigured to meet higher priority constraints in favor of lowerpriority constraints. In some embodiments, the controller 135 may beconfigured to take an action, e.g., generate an active power command ofa power asset that, while not satisfying a constraint instantaneously,may enable satisfaction of the constraint at a future time.

In an exemplary embodiment, constraints for the optimization may besegmented into 5 priority groups. It should be understood that thenumber of priority groups, as well as the grouping of constraints intosuch groups is illustrative only, and various embodiments may includeany number of constraints sorted in any suitable manner into anysuitable number of priority groups. A first, highest priority group ofconstraints may include the following. Net power provided by the hybridpower system 100 should match the power required by the load 110,whereby the net power may include both active and reactive power. Thepower provided by each power asset and/or power asset group 115 shouldnot exceed a respective power rating. The PV devices 151 and the gensets146 (and, for example, fuel cells or the like) should have non-negativeloading, e.g., no reverse loading. The PV group 150 should be associatedwith at least one anchor source. Power suppled from or to the power gridconnection 160 should not exceed import/export limits, respectively. Thegenset group 145 should operate with reactive power below apredetermined reactive power limit (e.g., that is based on a reactivecapacity curve associated with a genset 146 and/or the genset group,and/or on associated power factor range limits or thresholds). The powerassets should operate with an apparent power below a predeterminedapparent power limit. The hybrid power system 100 should operateaccording to predetermined resiliency and/or redundancy requirements,e.g., an excess of power assets to replace power assets that may operatebelow nominal. SOC for the energy storage system(s) 156 and/or energystorage system group 155 should be maintained within a safe range, e.g.,that is based on inputs to a management system (e.g., a batterymanagement system).

A second priority group may include a constraint that positive spinningreserve (e.g., additional available energy generating capacityachievable by increasing the power output of genset(s) already engagedin operation) is available in an amount that at least meets apredetermined or predicted threshold need, e.g., due to sudden PVdrop-off due to a cloud or sudden additional demand from the load 110. Athird priority group may include a constraint that negative spinningreserve (e.g., additional decrease in the the power output of operatinggenset(s) without halting their operation) is available in an amountthat at least meets a predetermined or predicted threshold need, e.g.,due to sudden PV curtailing or sudden reduced demand from the load 110.

A fourth priority group may include the following. SOC for the energystorage system(s) 156 and/or energy storage system group 155 should bemaintained within a predetermined target range, e.g., based ondegradation and life considerations. The predetermined target range maybe a narrower range than the safe range discussed above. The energystorage system(s) 156 and/or energy storage system group 155 should becharged as much as possible when SOC is below a threshold value. Theenergy storage system(s) 156 and/or energy storage system group 155should be discharged as much as possible when SOC is above a thresholdvalue.

A fifth priority group may include the following. A load on thegenset(s) 146 and/or the genset group 145 should be above a minimum loadthreshold, e.g., to reduce wet-stacking and/or preserve an operatinglifetime of the genset(s) 146 and/or the genset group 155. A load on thegenset(s) 146 and/or the genset group 145 should be below a maximumthreshold, e.g., to provide a safety margin to prevent overload and/orpreserve an operating lifetime of the genset(s) 146 and/or the gensetgroup 145.

It should be understood that the constraints and the grouping of theconstraints above is illustrative only, and that any suitableconstraints and/or grouping of such constraints may be used. Anysuitable technique for implementing such constraints in the optimizer175 may be used. For example, in some embodiments, each constraint mayact as a metric. In some embodiments, the metric(s) may be binary, e.g.,a value of zero for a satisfied constraint and a value of one for aviolated constraint. In some embodiments, the metric(s) may have a rangeof values corresponding to how well or to what extent the constraint(s)are satisfied. The value of the metric(s) may be associated with, e.g.,multiplied by, a weight value associated with the priority of theconstraint(s), e.g., higher weight values for higher priorityconstraints, and included in a cost function of the optimizer 175 as anadditional cost term, as discussed in more detail below.

In some embodiments, at least a portion of the constraints for the oneor more optimizations may be hard constraints, e.g., that defineoperating limitations that may not be violated. In some embodiments, atleast a portion of the constraints may be set, e.g., activated ordeactivated by a user 140, e.g., via the user device 105. In variousembodiments, constraints for the one or more optimizations may be based,for example, on customer and/or user specified options (e.g., via userdevice 105), and may include one or more of the following. The energystorage system group 155 is only to be charged via the PV group 150.Load on the genset group 145 is to be distributed proportionally acrossthe gensets 146 in the genset group 145 based on power rating. Load onthe energy storage system group 155 is to be distributed proportionallyacross the energy storage systems 156 in the energy storage system group155 based on power rating. Load on the energy storage system group 155is to be distributed proportionally across the energy storage systems156 in the energy storage system group 155 based on a current energycapacity. SOC of the energy storage systems 156 in the energy storagesystem group 155 should be balanced, e.g., based on energy storagesystems that are located proximate to each other, and/or on a totalaverage SOC for the energy storage system group 155.

Although depicted as separate components in FIG. 1 , it should beunderstood that a component or portion of a component in the hybridpower system 100 may, in some embodiments, be integrated with orincorporated into one or more other components. For example, a portionof the data resource device 125 may be integrated into the controller135 or the like. In another example, the controller 135 may beintegrated the user device 105. In some embodiments, operations oraspects of one or more of the components discussed above may bedistributed amongst one or more other components. Any suitablearrangement and/or integration of the various systems and devices of thehybrid power system 100 may be used.

FIG. 2 depicts a schematic 200 of an exemplary embodiment of thecontroller 135 of FIG. 1 in communication with the plurality of powerasset groups 115 and the data resource device 125. As illustrated inFIG. 2 , the optimizers 175 of the controller 135 may include asupervisory scheduler 205, a plurality of individual asset groupschedulers 210, a supervisory optimizer 215, and a plurality ofindividual asset group optimizers 220. The controller 135 may furtherinclude a genset controller 225. The data resource device 125 mayinclude a load manager 230, a load forecaster 235, a grid powercost/emissions forecaster 240, and a PV power generation forecaster 245.Each of the foregoing components is discussed in further detail below.

It should be understood that the distribution of such components acrossthe controller 135 and the data resource device 125 is exemplar only. Invarious embodiments, components such as the foregoing may be distributedin any suitable manner across any number of devices including thecontroller 135 and the data resource device 125. For example, in anillustrative embodiment, the schedulers 205 and 210 may be groupedtogether with the forecasters 235 and 240 in a first device, e.g., afirst prospective controller/device, and the optimizers 215 and 220 maybe grouped with the load manager 230 and genset controller 225 in asecond on-line controller/device.

Further, as noted above, in some embodiments, the controller 135includes multiple and/or distributed controllers. For example, in someembodiments, prospective components such as the supervisory scheduler205 and the plurality of individual asset group schedulers 210 may beimplemented on a first controller, and on-line components such as thesupervisory optimizer 215 and the plurality of individual asset groupoptimizers 220 may be implemented on a second controller. Any suitabledistribution of components across one or more controllers may be used.In an exemplary embodiment, the controller 135 and/or the data resourcedevice 125 may be at least partially implemented virtually, e.g., mayinclude virtualized components implemented on any suitable arrangementof hardware and/or software.

As noted above, the controller 135 may receive the optimal performancemap(s) and/or characteristics of the power asset(s) indicated by theoptimal performance map(s) from the data resource device 125. In someembodiments, the data resource device 125 may provide and/or update suchinformation periodically, e.g., at a rate less frequent than at whichthe optimizer 175 of the controller 135 is operated. The controller 135may use such information to generate and/or update one or more costfunctions for the optimizer 175, as discussed in more detail below.

The load manager 230 may monitor, track, and/or store informationassociated with the load 110. For example, the load manager 230 maystore on-line load data and/or historical data regarding differentamounts for the load 110 at different times and/or under differentcircumstances. In some embodiments, the load manager 230 may include anon-line load management component configured to, for example, shed aportion of the load 110 and/or re-add a previously shed portion of theload 110. In some embodiments, the load manager 230 may include a loadscheduler configured to assign a schedule to a portion of the load 110that may be scheduled, e.g., automatically and or via user interactionby the user device 105.

The load forecaster 235 may obtain, for example, the historical dataassociated with the load 110. Based on the historical data, the loadforecaster may generate a load forecast indicating predicted powerrequirements of the load 110 for a next future scheduling period such asthe next day. Any suitable forecasting technique may be used such as,for example, averaging over a plurality of historical schedulingperiods. In some embodiments, the load forecaster 235 may consideradditional information, such as ambient weather or temperatureconditions, or the like. In some embodiments, the load forecaster 235may apply one or more machine learning techniques, e.g., deep learning,to generate the load forecast data.

The grid power cost/emissions forecaster 240 may obtain grid powercost/emissions data for the grid power connection 160. The grid powercost/emissions data may include, for example, cost or revenue,respectively for import or export of power via the power grid connection160. In some embodiments, an entity associated with the power grid mayprovide such grid power cost/emissions data. In some embodiments, thegrid power cost/emissions forecaster 240 may predict the grid powercost/emissions data, e.g., based on historical grid power cost/emissionsdata. In some embodiments, the grid power cost/emissions forecaster 240may consider and/or apply incentives and/or negotiated rates whenpredicting the grid power cost/emissions data.

The PV power generation forecaster 245 may obtain historical weather andcloud data, e.g., for a geographical region associated with the PV group150, and may obtain historical and/or current power generated by the PVgroup 150. The PV power generation forecaster 245 may further obtain oneor more characteristics of the PV group 150, such as a standard powerrating of the PV devices 151 in the PV group when under a standardamount of irradiation at standard temperature. The PV power generationforecaster 245 may further obtain data for temperature and irradianceduring the next scheduling period. In some embodiments, such data may bebased on historical data, e.g., via averaging historical periods of thesame day, month, year, etc. via machine learning, or any other suitabletechnique. In some embodiments, such data may be obtained for currentconditions, e.g., via sensor(s) 120 such as a temperature sensor and/orcamera. In some embodiments, such data may be obtained or generatedbased on weather forecast data. Data for current conditions may beextrapolated to the next scheduling period. Based on the one or morecharacteristics of the PV group 150 and the temperature and irradiancedata, the PV power generation forecaster 245 may determine a maximumpower that may be available from the PV group 150. In some embodiments,the temperature and irradiance data may include data for multipleportions of the next scheduling period, e.g., each hour during a 24 hourday, and thus the maximum power for each portion may be determined.

The supervisory scheduler 205 may receive one or more of (i) the loadforecast data from the load forecaster 235, (ii) the grid powercost/emissions data from the grid power cost/emissions forecaster 240,and (iii) the PV power availability forecast from the PV powergeneration forecaster 245. The supervisory scheduler 205 may beconfigured to perform at least one prospective group optimization, e.g.,via the optimizer 175, to determine scheduled group active powercommands for the plurality of power asset groups 115 that optimize atotal operating cost of the hybrid power system over a predeterminedfuture time period, e.g., over a period of a next twenty-four hours fromthe time of the optimization, or the like. In an exemplary embodiment,the at least one prospective group optimization is performedperiodically, e.g., every few minutes, every hour, etc. The optimizer175 may apply a prospective supervisory cost function described infurther detail below.

The scheduled group active power commands for the plurality of powerasset groups 115 may include power group-level output commands for eachof the plurality of power asset groups 115. It should be understood thatthe scheduled group active power commands are prospective commandsassociated with the predetermined future time period, and are used, forexample, as guidance for the determination of on-line active powercommands to be implemented in an on-line, e.g., live or instantaneousfashion. In other words the scheduled group active power commands arebased on the moving horizon established by the predetermined future timeperiod, and thus account for future events within that time period,e.g., variance in the load 110, power asset group costs or availabilityor the like, while the on-line active power commands apply the scheduledactive power commands but also account for the on-line status andcondition of the power asset(s) and/or power asset group(s).

For the genset group 145, the scheduled group active power commands,e.g., form the supervisory scheduler 205, may include stop, continue,and/or start command schedules for individual gensets, and/or powercommand schedules for running the genset(s). Start commands may be basedon start/stop timers, and/or a priority order for starting and/orstopping the genset(s). As discussed in more detail below, it may bebeneficial to limit start and stop commands during periods when theoperating state of the hybrid power system 100 is highly transient,e.g., the load 110 is fluctuating or changing. The start/stop timers maydefine a period for certain conditions (such as variance in theoperating state being below a predetermined threshold for apredetermined period of time) must be exhibited by the hybrid powersystem 100 before the corresponding start or stop command is executed.In some embodiments, the start/stop timers may account for or beassociated with one or more penalties associated with operation of thegenset(s) for excessive start/stop frequency. In some embodiments, thepriority order for genset starts/stops may be based on the cost functionbeing optimized, e.g., that accounts for cost or emissions as discussedin further detail below, e.g., for similar gensets. In some embodiments,the priority order may be based on running hours, and/or one or moremaintenance considerations. In some embodiments, that the supervisoryscheduler 205 may include stop of an entire genset group or start of thefirst genset from a silent genset group using similar considerations asabove. For the electronic storage system group 155, the scheduled groupactive power commands may include charge, idle and/or discharge commandschedules, along with corresponding power command levels

Each individual asset group scheduler 210 may be associated with arespective one of the power asset groups 115. The individual asset groupscheduler 210 may receive the scheduled group active power commandscorresponding to its respective power asset group. The individual assetgroup scheduler 210 may perform at least one prospective individualoptimization, e.g., via the optimizer 175, to determine individualscheduled active power commands for each power asset within therespective power asset group. For example, the scheduled group activepower commands for the genset group 145 may define one or more periodsof time within a day during which power from the genset group 145 may berequired, and how much power that may be during each period. Theindividual scheduled active power commands generated by the individualasset group scheduler 210 associated with the genset group 145 maydefine when each genset 146 in the genset group 145 should be started orstopped in order to meet the scheduled group active power commands forthe genset group 145.

The individual active power commands associated with the energy storagesystem group 155 may include commands to charge, idle, or discharge eachof the energy storage systems 156, as well as an amount of power to becharged or discharged.

The individual asset group scheduler 210 may perform the at least oneprospective individual optimization periodically, e.g., after eachinstance of the at least one prospective group optimization.

The genset controller 225 may receive the individual active powercommands associated with the genset group 145, and may be configured totrack and/or manage the execution of the individual active powercommands associated with the genset group 145 and/or the order in whichgensets 145 are to be started or stopped.

The supervisory optimizer 210 may receive one or more of (i) the on-lineload data from the load manager 230, (ii) the order in which gensets 146are to be started or stopped from the genset controller 225, (iii)on-line operational status data for the plurality of power asset groups115 from the plurality of individual asset group optimizers 220, on-lineimport/export cost data for the power grid connection 160, or on-linefuel cost data. On-line operational status data may include, forexample, power being generated by each of the plurality of power assetgroups 115, SOC and/or degradation of the energy storage system group155, status, spin reserve, operating time, start/stop frequency of thegenset group 145, etc. The supervisory optimizer 215 may receive ordetermine energy costs for each of the plurality of power asset groups115, e.g., based on information received from the plurality ofindividual asset group optimizers 220 and/or the data resource device125. The supervisory optimizer 215 may be configured to perform at leastone on-line group optimization, e.g., via the optimizer 175, todetermine group on-line active power commands for the plurality of powerasset groups 115. The determined on-line active power commands may, forexample, be optimized so as to (i) account for variance between the loadforecast and the on-line load, (ii) account for variance between thepower availability forecast and the on-line power availability, and(iii) optimize the total operating cost of the hybrid power system. Inother words, as noted above, the on-line active power commands mayconsider and/or account for inputs from the prospective optimizedschedulers, e.g., based on forecasted loads, power availability andpricing, and may superimpose an on-line optimization based on varianceof actual loads, power capabilities and pricing, e.g., compared toassumptions made by the prospective optimizations. In an exemplaryembodiment, the at least one on-line group optimization is performedcontinuously, in real or near real time, and/or on an instantaneousbasis. The commands included in the group on-line active power commandsmay, for example, include the same or similar type of commands as thegroup prospective active power commands.

As noted above, the forecasts for the load 110, as well as the poweravailability and energy cost for each of the plurality of power assetgroups 115 may be estimated, extrapolated, predicted, or the like.However, on-line conditions of the hybrid power system 100, as well asexternal conditions such as weather, temperature, costs for fuel orimport/export of power via the power grid connection 160 may vary fromthe forecasts. Further, on-line capability of power assets may change,e.g., due to degradation or failure. The combination of the prospectiveoptimization(s) and the on-line optimization(s) may enable thecontroller 135 to use the scheduled group active power commands as abaseline to determine the on-line active power commands. Thiscombination may reduce the computational complexity of determining theon-line active power commands. Further, this combination may enable thecontroller 135 to combine the longer-term considerations of theprospective optimization with the shorter-term considerations of theon-line optimizations.

Each individual asset group optimizer 220 may be associated with arespective one of the power asset groups 115. The individual asset groupoptimizer 220 may receive the on-line group active power commandscorresponding to its respective power asset group. The individual assetgroup scheduler 210 may perform at least one on-line individualoptimization, e.g., via the optimizer 175, to determine individualon-line active power commands for each power asset within the respectivepower asset group. The commands included in the individual on-lineactive power commands may, for example, include the same or similar typeof commands as the individual prospective active power commands. Theindividual asset group optimizer 220 may further be configured tooperate the individual power assets of the corresponding power assetgroup.

The following are exemplary cost functions that may be used via theoptimizer 175, such as in one or more of the optimizations discussed inthe various examples and embodiments above. However, it should beunderstood that the following examples are illustrative only, and thatany suitable cost functions may be used.

Equation 1, below illustrates an exemplary cost function for an on-linegroup optimization.

C(x, u, t)=C _(energy)(x, u, t)+C _(degr)(x, u, t)+C _(maint)(x, u, t)+C_(gsswr)(x, u, t),  (1)

In the equation above, “C” is the total determined cost to be optimized,“x” is a state variable describing operating status and/orcharacteristics of the hybrid power system 100, “u” is a variableholding the different possible on-line group active power commands, “t”is a variable for time within a scheduling period. “C_(energy)” is thesum of energy costs for the plurality of power asset groups 115, and isdefined by equation 2:

C _(energy) =C _(g) +C _(es) +C _(pv)  (2)

whereby “C_(g)” is the determined energy cost for the genset group 145,“C_(es)” is the determined energy cost for the energy storage systemgroup 155, and “C_(pv)” is the determined energy cost for the PV group150. “C_(pv)” may be treated as negligible or null. “C_(g)” may bedefined by the optimal performance maps(s) generated by the dataresource device 125. “C_(es)” may be defined by differently based onwhether the energy storage system group 155 is to be charged (equation3) or discharged (equation 4).

C _(es)(t)=C _(b)(t)η_(ch)(P _(ch)(t))  (3)

C _(es)(t)=C _(b)(t)/η_(dis)(P _(dis)(t))  (4)

whereby “C_(b)” is the battery power cost (e.g., a weighted average ofthe cost of the power assets used to charge the energy storage systemgroup 155 over its SoC), “η_(ch)” and “η_(dis)” are charging anddischarging efficiency, respectively, and “P_(ch)” and “P_(dis)” are theamount of power charged or discharged, respectively. It should be notedthat the terms in equation 2 may also be broken up into separate powerterms and cost terms, and thus may alternatively be expressed asequation 2a:

C _(energy) =c _(g)(t)P _(g)(t)+c _(es)(t)P _(es)(t)+c _(pv)(t)P_(pv)(t)+c _(u)(t)P _(u)(t)+c _(fc)(t)P _(fc)(t)  (2a)

whereby the “fc” terms correspond to a power asset group for fuelcell(s) for embodiments including such a group.

Returning to equation 1, “C_(degr)” is the degradation cost of theenergy storage system group 155, and may be defined by equation (5):

C _(degr)(t)=C _(bd)(P _(es)(t))=[C _(bdcal)(t, P _(es)(t))+C_(bdcyc)(t, P _(es)(t))]  (5)

whereby “C_(bd)” is the cost of battery aging, “C_(b)”, “P_(es)” is thepower output from the energy storage system group 155, “C_(bd)” is thecalendar aging of the energy storage system group 155, and “C_(bdcyc)”is the cycling aging of the energy storage system group 155.

Calendar aging may be determined based on characteristics of the energystorage system group 155 including replacement cost at the end-of-lifeof an energy storage system 156, temperature effects on the energystorage system group 155, an amount of energy in the energy storagesystem group 155, and operating time of the energy storage system group155.

Cycling aging may be determined based on a function of battery capacityof the energy storage system group 155, the replacement cost, and bytracking DOD since a most recent change in SOC direction duringoperation of the energy storage system group 155. In some embodiments,cyclic degradation over the course of the future time period for theprospective optimizations may be accounted for via any suitabletechnique such as, for example, a rainflow count, e.g., via thesupervisory scheduler 205.

Returning again to equation 1, “C_(maint)(t)” is the maintenance costfor the genset group 145, and may be defined by equation (6):

C _(maint)(t)=C _(maint) R _(g)(t)  (6)

whereby “C_(maint)” is a linear curve fit for average maintenance costfor the genset group 145 over time as fed back from the individual assetgroup optimizer 215, e.g., based on running gensets 146 among the gensetgroup 145, and “R_(g)(t)” is the running status of the genset group 145(e.g., 1 for running, 0 for not running, i.e. silent group). It shouldbe understood, however, that in various embodiments, the averagemaintenance cost for the genset group 145 over time may not be linear.Thus, in some embodiments, the C_(maint) term may be a term that varieswith time according to any type of function, e.g., C_(maint) (t).

Returning yet again to equation 1, “C_(gsswr)” is start/stop frequencypenalty for the genset group 145, and may be determined based on amaximum number of starts for scheduling period, the current number ofstarts within the current scheduling period, the total maintenance costof the genset group 145 over it's operational lifetime, the totaloperational lifetime of the genset group 145, and the start/stop timerlengths.

In an example, an exemplary prospective group cost function may includean integral of an on-line individual optimization, such as the exampleabove, along with a penalty related to a change in SOC over thescheduling period, e.g., the future period of time from the time atwhich the prospective optimization is performed. In some embodiments,the prospective group cost function may further include a term relatedto sustaining and/or improving the SOC of the energy storage systemgroup 155 over the scheduling period. For example, in some use cases inwhich use is at least partially cyclical, e.g., in a vehicle, microgrid, or the like with daily cyclical loading and/or power generation(e.g., via the PV group 150), it may be beneficial for the SOC to bemaintained or improved over the course of the scheduling period. In someembodiments, such as some of the use cases with cyclical loading and/orpower generation, the cost for energy storage, e.g., “C_(es)”, may besimplified to and/or negated in favor of degradation cost for the energystorage system group 155. For instance, in some such embodiments, theenergy costs for charging the energy storage system group 155 may beaccounted for by the costs associated with other sources acting as thesource for the charge, such as the PV group 150.

In a further example, an exemplary on-line individual cost functionassociated with the genset group 145 may be determined by a summationacross each genset 146 for individual energy cost, maintenance cost, andstart/stop frequency penalty, such as in equation (7) below:

C _(g)(x, u, t)=Σ[C _(ge,i)(x, u, t)+C _(maint,i)(x, u, t)+C_(gsswr,i)(x, u, t)]  (7)

In an additional example, an exemplary prospective individual costfunction may include an integral taken over the scheduling period of theon-line individual cost function. As noted above, the costs determinedfor the on-line optimizations, such as in the exemplary equations abovepertains to a live, instantaneous, and/or on-line state of the powerassets, load, and environment, e.g., based on sensor 120. For theprospective optimizations, such as the exemplary prospective group costfunction and the prospective individual cost functions discussed above,the costs used for the optimization are integrals of cost forecasts,simulations, and/or predictions over the scheduling period, e.g., thepredetermined future period of time from the time at which theprospective optimization is performed. In an illustrative example, theprospective individual cost function for the genset group may include anintegration taken over the scheduling period of the individual energycosts for the scheduled operation of the gensets in the group, themaintenance costs due to the scheduled operation of the gensets in thegroup, and a start/stop frequency penalty assessed for each genset inthe group based on the scheduled starts and stops for that genset.

In another example, an exemplary on-line individual cost functionassociated with the energy storage system group 155 may be determined bya summation across each energy storage system 156 for individual energycost, degradation cost, and a balancing cost between SOCs of individualenergy storage system 156 and the energy storage system 155 as a whole(e.g., based on the constraint discussed above for SoC balancing amongvarious energy storage systems 156), such as in equation (8) below:

C _(es)(x, u, t)=Σ[C _(es,i)(x, u, t)+C _(degr,i)(x, u, t)]+w_(SOC)Σ[SOC_(i)(t)−SOC(t))]²  (8)

The prospective individual cost function for the energy storage systemgroup 155 may, as above, include an integral of the on-line individualcost function taken over the scheduling period, and may additionallyinclude a penalty related to a summation of the differences between theinitial and final SOC of each energy storage system 156 over the courseof the scheduling period.

Any suitable technique for performing optimizations according to thepresent disclosure may be used. For example, techniques that may be usedinclude particle swarm optimization, model predictive control,Hamiltonian (PMP/ECMS), Gradient methods, PSO, Mixed Integer ProgrammingVariants and/or a modified version of Equivalent ConsumptionMinimization Strategy.

In some embodiments, the on-line active power commands are automaticallyexecuted by the controller 135 on the power assets within the pluralityof power asset groups 115. In some embodiments, at least a portion ofthe on-line active power commands may be provided, e.g., asrecommendations, to the user device 105, whereby the user 140 may enterinstructions via the user device 105 to confirm, reject, modify, orreplace one or more of the on-line active power commands. In someembodiments, whether an on-line active power command is executedautomatically or sent as a recommendation is determined based on whetherand to what extent the constraints on the optimization are satisfied.

INDUSTRIAL APPLICABILITY

A hybrid power system, such as those described in one or more of theembodiments above, that is configured to one or more of combineprospective and on-line optimizations, or account for asset degradation,maintenance costs, and operating efficiency effects, may be used inconjunction with any appropriate load, and may act as a power system fora machine, vehicle, building, facility, power utility, or the like.

For example, a hybrid power system according to various aspects of thisdisclosure may act as a power system for a vehicle such as aconstruction vehicle, transport vehicle, or the like. Such a vehicle mayinclude a plurality of power asset groups such as, for example, two ormore of an internal combustion/genset group, an electronic storagesystem group, a fuel cell group, or the like. The load for the vehiclemay include power to move the vehicle, e.g., to a transmission connectedto wheels, treads, or the like, electronics for the vehicle, and/or amachine implement such as a shovel, lift, drill, mill, press, etc. Insome instances, a vehicle may further include one or more power assetgroups such as a power grid connection (e.g., via a trolley line orelectrified rail connection), or a photovoltaic group (e.g., viaphotovoltaic cells disposed on an exterior of the vehicle). It should beunderstood that any suitable combination of power asset groups may beused, and that each power asset group may include any suitable number ofpower assets, including a single power asset or many.

In another example, a hybrid power system according to various aspectsof this disclosure may act as a power system for a machine such as amanufacturing device, air-conditioning device, a computing device, etc.Such a machine may include a plurality of power asset groups such as,for example, two or more of a power grid connection, a genset group, anelectronic storage system group, a fuel cell group, a photovoltaicgroup, or the like. The load for the machine may include a mechanicalload (e.g., for machining or processing an article), electronics, or thelike.

In a further example, a hybrid power system according to various aspectsof this disclosure may act as a power plant for a facility, building,work-site, etc. Such a machine may include a plurality of power assetgroups such as, for example, two or more of a power grid connection, agenset group, an electronic storage system group, a fuel cell group, aphotovoltaic group, or the like. The load for the machine may includeload for electronics, machines, or the like associated with thefacility, building, work-site, etc. In some instances, the power plantmay be fixed, e.g., a fixed installment for a building or multi-buildingfacility. In some instances, the power play may be at least partiallymobile, e.g., an at least partially temporary power plant for aconstruction job-site that enables the job-site to use a local powergrid connection in combination with, for example, a genset group, anenergy storage group, a photovoltaic group, and/or the like.

A controller 135 utilizing a combination of prospective and on-lineoptimizations and/or that accounts for asset degradation, maintenancecosts, and operating efficiency effects may be applied, for example, toany power system that incorporates a plurality of power assets, e.g.,power assets of different types.

In one aspect, it may be desirable to reduce computational complexity ofoptimization of cost for hybrid power systems. In another aspect, it maybe beneficial to perform optimizations that account for both immediatefactors and longer term factors impacting the operational life of powerassets. In a further aspect, it may be beneficial to account foroperating characteristics of power assets such as degradation,maintenance, and operating efficiency, which may impact both immediateand long term efficiencies and costs of a hybrid power system.

FIG. 3 is a flowchart illustrating an exemplary method 300 for operatinga hybrid power system 100 according to one or more embodiments of thisdisclosure. While certain operations are described as being performed bycertain components, it should be understood that such operations may beperformed by different components and/or different combinations ofcomponents. Moreover, some operations may be executed at the instructionof and/or by the processor 170. Further, it should be understood thatone or more of the operations below may be performed concurrently and/orin an order different than the order presented below. Additionally, invarious embodiments, one or more of the following operations may beomitted, and/or additional operations may be added.

At block 305, a controller 135 of a hybrid power system 100 may obtain aload forecast of power needed by a load 110 of the hybrid power system100, e.g., over the course of a scheduling period such as, for example,a day.

At block 310, the controller may obtain a power availability forecastand an energy cost forecast for each power asset group of a plurality ofpower asset groups 115.

In some embodiments, the load forecast, the power availability forecast,the energy cost forecast, and the prospective optimization areperiodically updated. In some embodiments, at least a portion of one ormore of the power availability forecast or the energy cost forecast isbased on respective optimal performance maps for each individual powerasset that, for example, may be generated by a data resource device 125or the like. In some embodiments, the respective optimal performancemaps are periodically updated at a first rate that is slower than asecond rate for periodically updating the load forecast, the poweravailability forecast, the energy cost forecast, and a prospectiveoptimization performed by the controller 135, as discussed in furtherdetail below. In some embodiments, the plurality of power asset groups115 includes two or more of a genset group 145, an energy storage systemgroup 155, a photovoltaic group 150, and a power grid connection 160.

At block 315, the controller 135 may perform, e.g., via an optimizer175, at least one prospective optimization to determine scheduled activepower commands for the plurality of power asset groups 115 that optimizea total operating cost of the hybrid power system 100. In someembodiments, the at least one prospective optimization includes: aprospective group optimization that determines prospective group activepower commands for each power asset group; and a prospective individualoptimization that determines individual active power commands for eachpower asset within each group. In some embodiments, the at least oneprospective optimization accounts for one or more of asset degradation,asset maintenance cost, or asset operating efficiency cost. In someembodiments, the at least one prospective optimization is a constrainedoptimization. In some embodiments, the at least one prospectiveoptimization includes a plurality of soft constraints having differentpriority values.

At block 320, the controller 135 may track an on-line amount of powerneeded by the load 110 of the hybrid power system 100.

At block 325 the controller 135 may track an on-line power availabilityand an on-line energy cost for the plurality of power asset groups 115.

At block 330, the controller 135 may perform at least one on-lineoptimization to determine on-line active power commands for theplurality of power asset groups 115 that (i) account for variancebetween the load forecast and the on-line load, (ii) account forvariance between the power availability forecast and the on-line poweravailability, and (iii) optimize the total operating cost of the hybridpower system 100. In some embodiments, the at least one on-lineoptimization includes: an on-line group optimization that determineson-line group active power commands for each power asset group; and anon-line individual optimization that determines individual active powercommands for each power asset within each group. In some embodiments,the at least one on-line optimization accounts for one or more of assetdegradation, asset maintenance cost, or asset operating efficiency cost.In some embodiments, the at least one on-line optimization is aconstrained optimization. In some embodiments, the at least one on-lineoptimization includes a plurality of soft constraints having differentpriority values.

At block 335, the controller 135 may operate the plurality of powerassets groups 115 based on one or more of the scheduled active powercommands and the on-line active power commands.

FIG. 4 is a flowchart illustrating another exemplary method 400 foroperating a hybrid power system 100 according to one or more embodimentsof this disclosure.

At block 405, a controller 135 of a hybrid power system 100 may obtainload data for the hybrid power system 100.

At block 410, the controller 135 may obtain power availability data andenergy cost data for each power asset in each power asset group of aplurality of power asset groups 115. In some embodiments, the pluralityof power asset groups 115 includes two or more of a genset group 145, anenergy storage system group 155, a photovoltaic group 150, and a powergrid connection 160.

At block 415, the controller 135 may determine active power commands foreach power asset by performing at least one optimization, such that thedetermined active power commands optimize a total operating cost of thehybrid power system 100.

The at least one optimization may be based on at least one cost functionthat accounts for asset degradation, asset maintenance cost, assetoperation efficiency cost, and the energy cost data. In someembodiments, the asset degradation includes calendar aging and cyclingaging of the energy storage system group 155. In some embodiments, theasset maintenance cost for each genset 146 in the genset group 145 isbased on an operation time of the genset 146. In some embodiments, theoperation efficiency cost includes one or more of a state-of-chargebalance factor between energy storage systems 156 in the energy storagesystem group 155, a cumulative state-of-charge change for each energystorage system 156 in the energy storage system group 155, or astart/stop frequency cost for each genset 146 in the genset group 145.

The at least one optimization may be constrained by a plurality ofconstraints based on the load data, the power availability data, andcharacteristics of the power assets.

In some embodiments, the at least one optimization may include at leastone prospective optimization that is based on forecasts in the loaddata, the power availability data, and the energy cost data. In someembodiments, the at least one optimization may include at least oneon-line optimization that is based on on-line data in the load data, thepower availability data, and the energy cost data. In some embodiments,each of the at least one prospective optimization and the at least oneon-line optimization respectively include a group optimization thatdetermines active power commands for each asset power group on a grouplevel. In some embodiments, each of the at least one prospectiveoptimization and the at least one on-line optimization respectivelyinclude an individual asset optimization that determines active powercommands for each power asset within each power asset group, based onthe active power commands for the power asset group.

At block 420, the controller 135 may operate each power asset based onthe determined active power commands. In some embodiments, the hybridpower system 100 is configured such that an imbalance between an on-lineload of the hybrid power system and power generated by the plurality ofpower asset groups is fed into our out from the power grid connection,respectfully.

In some embodiments, operating a respective genset 146 in the gensetgroup 145 based on the determined active power commands includesdetermining whether an operating condition of the hybrid power system100 has been stable for a predetermined threshold period. Upondetermining that the hybrid power system 100 has been stable for thepredetermined threshold period, the controller 135 may wait for apredetermined period of time, and then starting or stop operation of therespective genset 146.

It should be understood that embodiments in this disclosure areexemplary only, and that other embodiments may include variouscombinations of features from other embodiments, as well as additionalor fewer features.

One or more embodiments of this disclosure may reduce operating cost ofa hybrid power system. One or more embodiments of this disclosure mayreduce a computing complexity of optimizing the cost of a hybrid powersystem. One or more embodiments of this disclosure may improve theoptimization of cost for a hybrid power system by accounting foroperating characteristics such as asset degradation, maintenance costs,and operating efficiency effect. One or more embodiments of thisdisclosure may account for both immediate effects of operating decisionsand longer term effects over the course of a scheduling period and/or anoperational lifetime of power assets in a hybrid power system.

In general, any process or operation discussed in this disclosure thatis understood to be computer-implementable, such as the processesillustrated in FIGS. 3 and 4 , may be performed by one or moreprocessors of a computer system, such any of the systems or devices inthe hybrid power system 100 of FIG. 1 , as described above. A process orprocess step performed by one or more processors may also be referred toas an operation or block. The one or more processors may be configuredto perform such processes by having access to instructions (e.g.,software or computer-readable code) that, when executed by the one ormore processors, cause the one or more processors to perform theprocesses. The instructions may be stored in a memory of the computersystem. A processor may be a central processing unit (CPU), a graphicsprocessing unit (GPU), or any suitable types of processing unit.

A computer system, such as a system or device implementing a process oroperation in the examples above, may include one or more computingdevices, such as one or more of the systems or devices in FIG. 1 . Oneor more processors of a computer system may be included in a singlecomputing device or distributed among a plurality of computing devices.A memory of the computer system may include the respective memory ofeach computing device of the plurality of computing devices.

FIG. 5 is a simplified functional block diagram of a computer 500 thatmay be configured as a device for executing the methods of FIGS. 2 and 3, according to exemplary embodiments of the present disclosure. Forexample, the computer 500 may be configured as the controller 135 and/oranother system according to exemplary embodiments of this disclosure. Invarious embodiments, any of the systems herein may be a computer 500including, for example, a data communication interface 520 for packetdata communication. The computer 500 also may include a centralprocessing unit (“CPU”) 502, in the form of one or more processors, forexecuting program instructions. The computer 500 may include an internalcommunication bus 508, and a storage unit 506 (such as ROM, HDD, SDD,etc.) that may store data on a computer readable medium 522, althoughthe computer 500 may receive programming and data via networkcommunications. The computer 500 may also have a memory 504 (such asRAM) storing instructions 524 for executing techniques presented herein,although the instructions 524 may be stored temporarily or permanentlywithin other modules of computer 500 (e.g., processor 502 and/orcomputer readable medium 522). The computer 500 also may include inputand output ports 512 and/or a display 510 to connect with input andoutput devices such as keyboards, mice, touchscreens, monitors,displays, etc. The various system functions may be implemented in adistributed fashion on a number of similar platforms, to distribute theprocessing load. Alternatively, the systems may be implemented byappropriate programming of one computer hardware platform.

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine-readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links, or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

While the disclosed methods, devices, and systems are described withexemplary reference to transmitting data, it should be appreciated thatthe disclosed embodiments may be applicable to any environment, such asa desktop or laptop computer, an automobile entertainment system, a homeentertainment system, etc. Also, the disclosed embodiments may beapplicable to any type of Internet protocol.

It should be appreciated that in the above description of exemplaryembodiments of the invention, various features of the invention aresometimes grouped together in a single embodiment, figure, ordescription thereof for the purpose of streamlining the disclosure andaiding in the understanding of one or more of the various inventiveaspects. This method of disclosure, however, is not to be interpreted asreflecting an intention that the claimed invention requires morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive aspects lie in less than allfeatures of a single foregoing disclosed embodiment. Thus, the claimsfollowing the Detailed Description are hereby expressly incorporatedinto this Detailed Description, with each claim standing on its own as aseparate embodiment of this invention.

Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe invention, and form different embodiments, as would be understood bythose skilled in the art. For example, in the following claims, any ofthe claimed embodiments can be used in any combination.

Thus, while certain embodiments have been described, those skilled inthe art will recognize that other and further modifications may be madethereto without departing from the spirit of the invention, and it isintended to claim all such changes and modifications as falling withinthe scope of the invention. For example, functionality may be added ordeleted from the block diagrams and operations may be interchanged amongfunctional blocks. Steps may be added or deleted to methods describedwithin the scope of the present invention.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other implementations, which fallwithin the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description. While variousimplementations of the disclosure have been described, it will beapparent to those of ordinary skill in the art that many moreimplementations are possible within the scope of the disclosure.Accordingly, the disclosure is not to be restricted except in light ofthe attached claims and their equivalents.

What is claimed is:
 1. A method of operating a hybrid power system,comprising: obtaining load data for the hybrid power system; obtainingpower availability data and energy cost data for each power asset ineach power asset group of a plurality of power asset groups; anddetermining active power commands for each power asset by performing atleast one optimization, such that the determined active power commandsoptimize a total operating cost of the hybrid power system, wherein: theat least one optimization is based on at least one cost function thataccounts for asset degradation, asset maintenance cost, asset operationefficiency cost, and the energy cost data; and the at least oneoptimization is constrained by a plurality of constraints based on theload data, the power availability data, and characteristics of the powerassets; and operating each power asset based on the determined activepower commands.
 2. The method of claim 1, wherein the plurality of powerasset groups includes two or more of a genset group, an energy storagesystem group, a photovoltaic group, or a power grid connection.
 3. Themethod of claim 2, wherein asset degradation includes calendar aging andcycling aging of the energy storage system group.
 4. The method of claim2, wherein the asset maintenance cost for each genset in the gensetgroup is based on an operation time of the genset.
 5. The method ofclaim 2, wherein the operation efficiency cost includes one or more of astate-of-charge balance factor between energy storage systems in theenergy storage system group, a cumulative state-of-charge change foreach energy storage system in the energy storage system group, or astart/stop frequency cost for each genset in the genset group.
 6. Themethod of claim 2, wherein operating a respective genset in the gensetgroup based on the determined active power commands includes:determining whether an operating condition of the hybrid power systemhas been stable for a predetermined threshold period; and upondetermining that the hybrid power system has been stable for thepredetermined threshold period: waiting for a predetermined period oftime; and after waiting for the predetermined period of time, startingor stopping operation of the respective genset.
 7. The method of claim1, wherein the at least one optimization includes: at least oneprospective optimization that is based on forecasts in the load data,the power availability data, and the energy cost data; and at least oneon-line optimization that is based on on-line data in the load data, thepower availability data, and the energy cost data
 8. The method of claim7, wherein each of the at least one prospective optimization and the atleast one on-line optimization respectively include a group optimizationthat determines active power commands for each asset power group on agroup level; and an individual asset optimization that determines activepower commands for each power asset within each power asset group, basedon the active power commands for the power asset group
 9. The method ofclaim 1, wherein: the hybrid power system includes a power gridconnection; and the hybrid power system is configured such that animbalance between an on-line load of the hybrid power system and powergenerated by the plurality of power asset groups is fed into or out fromthe power grid connection, respectfully.
 10. The method of claim 9,wherein the at least one cost function further accounts for import costsand export costs for feeding power into and out form the power gridconnection, respectfully.
 11. The method of claim 1, wherein: the hybridpower system is a power system of a vehicle; and the plurality of powerasset groups includes an energy storage system group and one or more ofan internal combustion or genset group or a power grid connection group.12. The method of claim 1, wherein: the hybrid power system is a powersystem of a building or facility; and the plurality of power assetgroups includes a power grid connection, a genset group, and an energystorage system group.
 13. A controller for a hybrid power system,comprising: at least one memory storing instructions; and at least oneprocessor operatively connected to the memory, and configured to executethe instructions to perform operations, including: obtaining load datafor the hybrid power system; obtaining power availability data andenergy cost data for each power asset in each power asset group of aplurality of power asset groups; and determining active power commandsfor each power asset by performing at least one optimization, such thatthe determined active power commands optimize a total operating cost ofthe hybrid power system, wherein: the at least one optimization is basedon at least one cost function that accounts for asset degradation, assetmaintenance cost, asset operation efficiency cost, and the energy costdata; and the at least one optimization is constrained by a plurality ofconstraints based on the load data, the power availability data, andcharacteristics of the power assets; and operating each power assetbased on the determined active power commands.
 14. The controller ofclaim 13, wherein the plurality of power asset groups includes two ormore of a genset group, an energy storage system group, a photovoltaicgroup, or a power grid connection.
 15. The controller of claim 13,wherein: the at least one optimization includes: at least oneprospective optimization that is based on forecasts in the load data,the power availability data, and the energy cost data; and at least oneon-line optimization that is based on on-line data in the load data, thepower availability data, and the energy cost data; and each of the atleast one prospective optimization and the at least one on-lineoptimization respectively include a group optimization that determinesactive power commands for each asset power group on a group level; andan individual asset optimization that determines active power commandsfor each power asset within each power asset group, based on the activepower commands for the power asset group
 16. The controller of claim 13,wherein: the hybrid power system includes a power grid connection; thehybrid power system is configured such that an imbalance between anon-line load of the hybrid power system and power generated by theplurality of power asset groups is fed into or out from the power gridconnection, respectfully; and the at least one cost function furtheraccounts for import costs and export costs for feeding power into andout form the power grid connection, respectfully.
 17. The controller ofclaim 13, wherein either: (i) the hybrid power system is a power systemof a vehicle, and the plurality of power asset groups includes an energystorage system group and one or more of an internal combustion or gensetgroup or a power grid connection group; or (ii) the hybrid power systemis a power system of a building or facility, and the plurality of powerasset groups includes a power grid connection, a genset group, and anenergy storage system group.
 18. A hybrid power system, comprising: aplurality of power asset groups that includes two or more of a gensetgroup, an energy storage system group, a photovoltaic group, or a powergrid connection; and a controller that includes: at least one memorystoring instructions; and at least one processor operatively connectedto the memory, and configured to execute the instructions to performoperations, including: obtaining load data for the hybrid power system;obtaining power availability data and energy cost data for each powerasset in each power asset group of a plurality of power asset groups;and determining active power commands for each power asset by performingat least one optimization, such that the determined active powercommands optimize a total operating cost of the hybrid power system,wherein:  the at least one optimization is based on at least one costfunction that accounts for asset degradation, asset maintenance cost,asset operation efficiency cost, and the energy cost data; and  the atleast one optimization is constrained by a plurality of constraintsbased on the load data, the power availability data, and characteristicsof the power assets; and operating each power asset based on thedetermined active power commands.
 19. The hybrid power system of claim18, wherein: the hybrid power system is a power system of a vehicle; andthe plurality of power asset groups includes an energy storage systemgroup and one or more of an internal combustion or genset group or apower grid connection group.
 20. The hybrid power system of claim 18,wherein: the hybrid power system is a power system of a building orfacility; and the plurality of power asset groups includes a power gridconnection, a genset group, and an energy storage system group.